Machine Learning for Beginners

Machine Learning




Machine learning is the ability of artificial intelligence to learn and adapt without being explicitly programmed to perform a pre-prescribed outcome. It is a machine's ability to act on its own, to learn. Many forms of artificial intelligence use a wide variety of tactics to employ this, including pattern recognition and unsupervised learning algorithms. Currently, there is a lot of excitement and focus on machine learning in the world of programming, as it is often interpreted to imply a limitless future in artificial intelligence: a world in which machines could adapt and respond to a wide variety of stimuli and factors without direct programming response from a human. A world in which machines could learn and adapt on their own.
Often, machine learning is seen as the missing "human" element in machines; the ability for machines to start to fill the gap between what makes us human and what makes machines; a sense of responsiveness and flexibility in a given environment and a set of circumstances. By and large, the goal of learning is to be able to generalize: to take a set of lived circumstances, and to be able to extrapolate a sense of patterning about what the future holds, and how the person should respond to similar given situations. Machine learning is similar; it is the attempt to program machines so that they can generalize about future possibilities and probabilities based on data sets. In short- machine learning is the quest to get machines to think like humans. This takes a wide variety of forms and is used for a wide variety of purposes.
In the following book, we will explore the history of machine learning, the academic and scientific elements that make up the study, as well as touching on this moral and philosophical space that they occupy.




In this book you will find:




What is Machine Learning?The History of Machine LearningExamples of Machine LearningHow Does Machine Learning Work?Common Approaches and Terms in Machine LearningTheoretical Computer ScienceComputational Learning Theory/Association Rule LearningPattern RecognitionDeep LearningInduction Logic ReasoningNeural networksExpert SystemsNaive Bayes SystemsUnsupervised and supervised learning algorithmsDecision TreesRandom Decision ForestsThe Moral and Philosophical Implications of Machine Learning